scholarly journals CXCL10 is a Tumor Microenvironment and Immune Infiltration Related Prognostic Biomarker in Pancreatic Adenocarcinoma

2021 ◽  
Vol 8 ◽  
Author(s):  
Huimin Huang ◽  
Wangxiao Zhou ◽  
Renpin Chen ◽  
Bingfeng Xiang ◽  
Shipeng Zhou ◽  
...  
Keyword(s):  
Tumor Microenvironment ◽  
Pancreatic Adenocarcinoma ◽  
Cox Regression ◽  
Predictive Biomarker ◽  
Prognostic Indicator ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis ◽  
Immune Infiltration ◽  
Protein Protein Interaction ◽  
Novel Biomarkers

Pancreatic adenocarcinoma (PAAD) is the 10th most common cancer worldwide and the outcomes for patients with the disease remain extremely poor. Precision biomarkers are urgently needed to increase the efficiency of early diagnosis and to improve the prognosis of patients. The tumor microenvironment (TME) and tumor immune infiltration are thought to impact the occurrence, progression, and prognosis of PAAD. Novel biomarkers excavated originating from the TME and immune infiltration may be effective in predicting the prognosis of PAAD patients. In the current study, the ESTIMATE and CIBERSORT algorithms were applied to estimate the division of immune and stromal components and the proportion of tumor-infiltrating immune cells in 182 PAAD cases downloaded from The Cancer Genome Atlas database. Intersection analyses of the Protein-Protein Interaction networks and Cox regression analysis identified the chemokine (CXC-motif) ligand 10 (CXCL10) as a predictive biomarker. We verified that CXCL10 in the TME negatively correlates with prognosis in PAAD and positively correlates with tumor cell differentiation. GSE62452 from the GEO database and cumulative survival analysis were performed to validate CXCL10 expression as an independent prognostic indicator. We also found that memory B cells, regulatory T cells, and macrophages M0 and M1 were correlated with the expression of CXCL10 indicating that expression of CXCL10 influenced the immune activity of the TME. Our data suggest that CXCL10 is beneficial as a prognostic indicator in PAAD patients and highlights the potential for immune targeted therapy in the treatment of PAAD.

scholarly journals GNG7 and ADCY1 as diagnostic and prognostic biomarkers for pancreatic adenocarcinoma through bioinformatic-based analyses

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Youfu Zhang ◽  
Jinran Yang ◽  
Xuyang Wang ◽  
Xinchang Li
Keyword(s):  
Survival Analysis ◽  
Pancreatic Adenocarcinoma ◽  
Cox Regression ◽  
Malignant Tumors ◽  
The Cancer Genome Atlas ◽  
Prognostic Biomarkers ◽  
Cox Regression Analysis ◽  
Protein Protein Interaction ◽  
Kegg Analysis ◽  
Upregulated Genes

AbstractPancreatic adenocarcinoma (PAAD) is one of the most lethal malignant tumors in the world. The GSE55643 and GSE15471 microarray datasets were downloaded to screen the diagnostic and prognostic biomarkers for PAAD. 143 downregulated genes and 118 upregulated genes were obtained. Next, we performed gene ontology (GO) and The Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis on these genes and constructed a protein–protein interaction (PPI) network. We screened out two important clusters of genes, including 13 upregulated and 5 downregulated genes. After the survival analysis, 3 downregulated genes and 10 upregulated genes were identified as the selected key genes. The KEGG analysis on 13 selected genes showed that GNG7 and ADCY1 enriched in the Pathway in Cancer. Next, the diagnostic and prognostic value of GNG7 and ADCY1 was investigated using independent cohort of the Cancer Genome Atlas (TCGA), GSE84129 and GSE62452. We observed that the expression of the GNG7 and ADCY1 was decreased in PAAD. The diagnostic receiver operating characteristic (ROC) analysis indicated that the GNG7 and ADCY1 could serve as sensitive diagnostic markers in PAAD. Survival analysis suggested that expression of GNG7, ADCY1 were significantly associated with PAAD overall survival (OS). The multivariate cox regression analysis showed that the expression of GNG7, ADCY1 were independent risk factors for PAAD OS. Our study indicated GNG7 and ADCY1 may be potential diagnostic and prognostic biomarkers in patients with PAAD.

Identification of the Pyroptosis-related Signature for Predicting Prognosis in Cervical Cancer

2021 ◽  
Author(s):  
Cankun Zhou ◽  
Chaomei Li ◽  
Yuhua Zheng ◽  
Xiaochun Liu
Keyword(s):  
Cervical Cancer ◽  
Tumor Microenvironment ◽  
Cox Regression ◽  
Experimental Studies ◽  
Risk Groups ◽  
Core Gene ◽  
Low Risk ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Immune Infiltration

Abstract Background: Cervical cancer (CC) is one of the most common malignancies in gynecology. There is still a lack of specific biomarkers for the diagnosis and prognosis of CC. Pyroptosis is one of the methods of programmed cell death, and its various components are related to the occurrence, invasion, and metastasis of tumors. However, the role of pyroptosis in CC has not yet been elucidated.Methods: This study focuses on the development of a prognostic signature associated with pyroptosis for CC patients using integrated bioinformatics to elucidate the relationship between the signature and the tumor microenvironment and immune response.Results: We identified a prognostic signature based on eight pyroptosis-related genes (PRGs), with better prognostic survival in the low-risk group (P<0.05) and AUC values greater than 0.7. The results of the multi-factor Cox regression analysis indicated that the signature could be used as an independent prognostic factor, and both the DCA and the Nomogram suggested that the prognostic signature had good predictive power. Interestingly, this prognostic signature can also be applied to multiple tumors. In addition, the tumor microenvironment and immune infiltration status were significantly different between high and low-risk groups (P<0. 05). The core gene GZMB was screened and the CC-associated GZMB/ miR-378a/TRIM52-AS1 regulatory axis was constructed.Conclusion: The study successfully established the prognostic signature based on eight PRGs and reflected their tumor microenvironment and immune infiltration. The GZMB/ miR-378a/TRIM52-AS1 regulatory axis may play an important regulatory role in the development of CC, and further experimental studies are needed to validate these results subsequently.

scholarly journals Establishment of an Immune-Related Gene Signature for Risk Stratification for Patients with Glioma

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Jimin He ◽  
Chun Zeng ◽  
Yong Long
Keyword(s):  
Risk Stratification ◽  
Prognostic Model ◽  
Cox Regression ◽  
Prognostic Indicator ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Survival Prediction ◽  
Cox Regression Analysis ◽  
Immune Related Genes ◽  
Genome Atlas

Glioma is a frequently seen primary malignant intracranial tumor, characterized by poor prognosis. The study is aimed at constructing a prognostic model for risk stratification in patients suffering from glioma. Weighted gene coexpression network analysis (WGCNA), integrated transcriptome analysis, and combining immune-related genes (IRGs) were used to identify core differentially expressed IRGs (DE IRGs). Subsequently, univariate and multivariate Cox regression analyses were utilized to establish an immune-related risk score (IRRS) model for risk stratification for glioma patients. Furthermore, a nomogram was developed for predicting glioma patients’ overall survival (OS). The turquoise module ( cor = 0.67 ; P < 0.001 ) and its genes ( n = 1092 ) were significantly pertinent to glioma progression. Ultimately, multivariate Cox regression analysis constructed an IRRS model based on VEGFA, SOCS3, SPP1, and TGFB2 core DE IRGs, with a C-index of 0.811 (95% CI: 0.786-0.836). Then, Kaplan-Meier (KM) survival curves revealed that patients presenting high risk had a dismal outcome ( P < 0.0001 ). Also, this IRRS model was found to be an independent prognostic indicator of gliomas’ survival prediction, with HR of 1.89 (95% CI: 1.252-2.85) and 2.17 (95% CI: 1.493-3.14) in the Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) datasets, respectively. We established the IRRS prognostic model, capable of effectively stratifying glioma population, convenient for decision-making in clinical practice.

scholarly journals FABP4 Might be an Indicator of the Status of Tumor Microenvironment in Colorectal Cancer

2021 ◽  
Author(s):  
Lijun Ning ◽  
Yuqing Yan ◽  
Tianying Tong ◽  
Ziyun Gao ◽  
Zhe Cui ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
T Cells ◽  
Tumor Microenvironment ◽  
Cox Regression ◽  
Nk Cell ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Fatty Acid Binding ◽  
Cox Regression Analysis ◽  
The Status

Abstract Background: As tumor microenvironment (TME) play an indispensable role in tumorigenesis of colorectal cancer, this study performs a bunch of bioinformatics analysis to identify the indicator of the status of TME in Colorectal cancer (CRC). Results: In the presented study, we applied CIBERSORT and ESTIMATE computational methods to calculate the proportion of tumor-infiltrating immune cells (TICs) and the amount of immune and stromal components in 444 COAD-READ cases from The Cancer Genome Atlas (TCGA) database. The differentially expressed genes (DEGs) were analyzed by COX regression analysis and protein–protein interaction (PPI) network construction. Then, fatty acid-binding protein four ( FABP4 ) was determined as a predictive factor by the intersection analysis of univariate COX and PPI. Further analysis revealed that FABP4 expression was positively correlated with the clinical pathologic characteristics (clinical stage, distant metastasis) and negatively correlated with the survival of CRC patients. Gene Set Enrichment Analysis (GSEA) showed that the genes in the high-expression FABP4 group were mainly enriched in immune-related activities. In the low-expression FABP4 group, the genes were enriched in metabolic pathways. CIBERSORT analysis for the proportion of TICs revealed that NK cell, CD4 + T cells and CD8 + T cells were negatively correlated with FABP4 expression, suggesting that FABP4 might be a potential prognostic factor of CRC patients. Conclusion: Our study has developed a new biomarker (FABP4) that can predict the status of tumor microenvironment in Colorectal cancer. Keywords: FABP4, tumor microenvironment, ESTIMATE, CIBERSORT, colorectal cancer

scholarly journals m6A RNA Methylation Regulators Impact Prognosis and Tumor Microenvironment in Renal Papillary Cell Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Lianze Chen ◽  
Baohui Hu ◽  
Xinyue Song ◽  
Lin Wang ◽  
Mingyi Ju ◽  
...  
Keyword(s):  
Tumor Microenvironment ◽  
Cell Carcinoma ◽  
Risk Model ◽  
Cox Regression ◽  
Expression Patterns ◽  
The Cancer Genome Atlas ◽  
Rna Modification ◽  
Lasso Regression ◽  
Cox Regression Analysis ◽  
Rna Methylation

Accumulating evidence has proven that N6-methyladenosine (m6A) RNA methylation plays an essential role in tumorigenesis. However, the significance of m6A RNA methylation modulators in the malignant progression of papillary renal cell carcinoma (PRCC) and their impact on prognosis has not been fully analyzed. The present research set out to explore the roles of 17 m6A RNA methylation regulators in tumor microenvironment (TME) of PRCC and identify the prognostic values of m6A RNA methylation regulators in patients afflicted by PRCC. We investigated the different expression patterns of the m6A RNA methylation regulators between PRCC tumor samples and normal tissues, and systematically explored the association of the expression patterns of these genes with TME cell-infiltrating characteristics. Additionally, we used LASSO regression to construct a risk signature based upon the m6A RNA methylation modulators. Two-gene prognostic risk model including IGF2BP3 and HNRNPC was constructed and could predict overall survival (OS) of PRCC patients from the Cancer Genome Atlas (TCGA) dataset. The prognostic signature-based risk score was identified as an independent prognostic indicator in Cox regression analysis. Moreover, we predicted the three most significant small molecule drugs that potentially inhibit PRCC. Taken together, our study revealed that m6A RNA methylation regulators might play a significant role in the initiation and progression of PRCC. The results might provide novel insight into exploration of m6A RNA modification in PRCC and provide essential guidance for therapeutic strategies.

scholarly journals The N6-Methyladenosine- (m6A-) Associated Genes Act as Strong Key Biomarkers for the Prognosis of Pancreatic Adenocarcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Fei Li ◽  
Ping Zhang
Keyword(s):  
Pancreatic Adenocarcinoma ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Pancreatic Tumors ◽  
Molecular Characteristics ◽  
Main Role ◽  
Consensus Clustering ◽  
Cox Regression Analysis ◽  
Rna Methylation ◽  
M6a Rna Methylation

Background. Pancreatic adenocarcinoma (PAAD) has become the major cause of cancer-related deaths globally. The m6A (N6-methyladenosine) alteration plays a crucial function in carcinogenesis and tumor progression. The role of genes related to m6A and their expression level in pancreatic cancer is not identified yet. The objective of this research analysis is a demonstration of the m6A RNA methylation regulators based as biomarkers for the PAAD diagnosis. Methods. About 23 extensively reported m6A RNA methylation regulators were identified through the Cancer Genome Atlas (TCGA) database. This identification was based on consensus clustering analysis, protein-protein integration (PPI) analysis, risk prognostic model, Cox-regression analysis, String Spearman analysis, and LASSO Cox-regression. Results. Herein, we conclude that 23 m6A methylation regulators have a strong link with the clinical and molecular characteristics of PAAD. The three subgroups (1/2) of pancreatic adenocarcinoma were identified using the clustering of 23 m6A regulators. Subgroup cluster 2 had a lower survival rate than the subgroup of cluster 1, and the difference in grades between the two groups was substantial. An assessment was performed using the 23 reported m6A methylation regulators. Eight of these can be used as independent PAAD prognostic markers. The consequences of variable IGF2BP3 expression in PAAD were then investigated further. Conclusions. The key finding of this study was that the m6A methylation regulator gene has the main role in pancreatic tumors, and it may be used as a biomarker in the prognosis of the PAAD and for therapy purposes.

scholarly journals Identification of a Gene Prognostic Model of Gastric Cancer Based on Analysis of Tumor Mutation Burden

2021 ◽  
Vol 27 ◽  
Author(s):  
Weijun Ma ◽  
Weidong Li ◽  
Lei Xu ◽  
Lu Liu ◽  
Yu Xia ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Prognostic Model ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Survival Outcomes ◽  
Activated T Cells ◽  
Single Nucleotide ◽  
Cox Regression Analysis ◽  
Immune Infiltration ◽  
Surgical Methods

Introduction: Gastric cancer is one of the most common cancers. Although some progress has been made in the treatment of gastric cancer with the improvement of surgical methods and the application of immunotherapy, the prognosis of gastric cancer patients is still unsatisfactory. In recent years, there has been increasing evidence that tumor mutational load (TMB) is strongly associated with survival outcomes and response to immunotherapy. Given the variable response of patients to immunotherapy, it is important to investigate clinical significance of TMB and explore appropriate biomarkers of prognosis in patients with gastric cancer (GC).Material and Methods: All data of patients with gastric cancer were obtained from the database of The Cancer Genome Atlas (TCGA). Samples were divided into two groups based on median TMB. Differently expressed genes (DEGs) between the high- and low-TMB groups were identified and further analyzed. We identified TMB-related genes using Lasso, univariate and multivariate Cox regression analysis and validated the survival result of 11 hub genes using Kaplan-Meier Plotter. In addition, “CIBERSORT” package was utilized to estimate the immune infiltration.Results: Single nucleotide polymorphism (SNP), C &gt; T transition were the most common variant type and single nucleotide variant (SNV), respectively. Patients in the high-TMB group had better survival outcomes than those in the low-TMB group. Besides, eleven TMB-related DEGs were utilized to construct a prognostic model that could be an independent risk factor to predict the prognosis of patients with GC. What’s more, the infiltration levels of CD4+ memory-activated T cells, M0 and M1 macrophages were significantly increased in the high-TMB group compared with the low-TMB group.Conclusions: Herein, we found that patients with high TMB had better survival outcomes in GC. In addition, higher TMB might promote immune infiltration, which could provide new ideas for immunotherapy.

lncRNA and mRNA signature for prognosis prediction of glioblastoma

2020 ◽  
Vol 16 (13) ◽  
pp. 837-848 ◽  
Author(s):  
Guohong Liu ◽  
Yunbao Pan ◽  
Yueying Li ◽  
Haibo Xu
Keyword(s):  
Cox Regression ◽  
Expression Profiles ◽  
Tissue Expression ◽  
The Cancer Genome Atlas ◽  
Normal Brain ◽  
Multiple Gene ◽  
Cox Regression Analysis ◽  
R Language ◽  
Cancer Genome Atlas ◽  
Novel Biomarkers

Aims: We aimed to find out potential novel biomarkers for prognosis of glioblastoma (GBM). Materials & methods: We downloaded mRNA and lncRNA expression profiles of 169 GBM and five normal samples from The Cancer Genome Atlas and 129 normal brain samples from genotype-tissue expression. We use R language to perform the following analyses: differential RNA expression analysis of GBM samples using ‘edgeR’ package, survival analysis taking count of single or multiple gene expression level using ‘survival’ package, univariate and multivariate Cox regression analysis using Cox function plugged in ‘survival’ package. Gene ontology and Kyoto encyclopedia of genes and genomes pathway analysis were performed using FunRich tool online. Results and conclusion: We obtained differentially DEmRNAs and DElncRNAs in GBM samples. Most prognostically relevant mRNAs and lncRNAs were filtered out. ‘GPCR ligand binding’ and ‘Class A/1’ are found to be of great significance. In short, our study provides novel biomarkers for prognosis of GBM.

scholarly journals Identification of Novel Diagnostic Biomarkers in Prostate Adenocarcinoma Based on the Stromal-Immune Score and Analysis of the WGCNA and ceRNA Network

2022 ◽  
Vol 2022 ◽  
pp. 1-10
Author(s):  
Tengfei Zhang ◽  
Yaxuan Wang ◽  
Yiming Dong ◽  
Lei Liu ◽  
Yikai Han ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Tumor Microenvironment ◽  
Cox Regression ◽  
Differentially Expressed Gene ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Prognostic Biomarkers ◽  
Mechanism Study ◽  
Cox Regression Analysis ◽  
Immune Score

Prostate cancer is still a significant global health burden in the coming decade. Novel biomarkers for detection and prognosis are needed to improve the survival of distant and advanced stage prostate cancer patients. The tumor microenvironment is an important driving factor for tumor biological functions. To investigate RNA prognostic biomarkers for prostate cancer in the tumor microenvironment, we obtained relevant data from The Cancer Genome Atlas (TCGA) database. We used the bioinformatics tools Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) algorithm and weighted coexpression network analysis (WGCNA) to construct tumor microenvironment stromal-immune score-based competitive endogenous RNA (ceRNA) networks. Then, the Cox regression model was performed to screen RNAs associated with prostate cancer survival. The differentially expressed gene profile in tumor stroma was significantly enriched in microenvironment functions, like immune response, cancer-related pathways, and cell adhesion-related pathways. Based on these differentially expressed genes, we constructed three ceRNA networks with 152 RNAs associated with the prostate cancer tumor microenvironment. Cox regression analysis screened 31 RNAs as the potential prognostic biomarkers for prostate cancer. The most interesting 8 prognostic biomarkers for prostate cancer included lncRNA LINC01082, miRNA hsa-miR-133a-3p, and genes TTLL12, PTGDS, GAS6, CYP27A1, PKP3, and ZG16B. In this systematic study for ceRNA networks in the tumor environment, we screened out potential biomarkers to predict prognosis for prostate cancer. Our findings might apply a valuable tool to improve prostate cancer clinical management and the new target for mechanism study and therapy.

scholarly journals Identification of Biomarkers Related to Prognosis of Glioblastoma and Their Correlation with Immune Infiltration Cells

2021 ◽  
Author(s):  
Wencong Ding ◽  
Guoqiang Jiang ◽  
Songkai Long ◽  
Yongshi Liao ◽  
Jia Liu
Keyword(s):  
Poor Prognosis ◽  
Cox Regression ◽  
Interaction Network ◽  
Gene Expression Omnibus ◽  
Venn Diagram ◽  
Cox Regression Analysis ◽  
Immune Infiltration ◽  
Protein Protein Interaction ◽  
Tumor Associated Macrophage ◽  
M1 Macrophage

Abstract Glioblastoma (GBM) is the most malignant of all known intracranial tumors, meanwhile most patients have a poor prognosis. In order to improve the poor prognosis of GBM patients as much as possible, it is specifically significant to identify biomarkers related to the gene diagnosis and gene therapy. Herein, a total of 343 GBM specimens and 259 non-tumor specimens were collected from four Gene Expression Omnibus (GEO) datasets and TCGA database, and then analyzed the differentially expressed genes (DEGs) from the above data. Through Venn diagram analysis, 54 common upregulated DEGs and 22 common downregulated DEGs were triumphantly recognized. On account of the degree of formation communication in Protein-protein interaction network (PPIN), the 10 upregulated central genes had been ranked, incorporating LOX, IGFBP3, CD44, TIMP1, FN1, VEGFA, POSTN, COL1A1, COL1A2 and COL3A1. By combining the expression levels and the clinical features of GBM, four hub genes (TIMP1, FN1, POSTN and LOX) were significantly up-regulated and related to poor prognosis. Meanwhile, univariate and multivariate Cox regression analysis results suggested that TIMP1 could be one of the independent prognostic factors for GBM patients. Subsequently, TIMP1 was particularly correlated with the immune marker of macrophage M1, macrophage M2, Neutrophils, tumor associated macrophage and Tregs. In conclusion, these findings investigated that TIMP1 might be a new biomarker to determine prognosis and immune infiltration of GBM patients.

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